Interpretation and reporting of predictive or diagnostic machine-learning research in Trauma & Orthopaedics. (1st December 2021)

• Record Type: Journal Article
• Authors: Farrow, Luke; Zhong, Mingjun; Ashcroft, George Patrick; Anderson, Lesley; Meek, R. M. Dominic
• Is Part Of: Bone & joint journal. Volume 103B:Number 12(2021)
• Page Start: 1754
• Page End: 1758
• Publication Date: 2021-12-01
• Subjects: Machine learning -- Artificial intelligence -- Orthopaedics -- Trauma -- Prediction -- Orthopaedic surgery -- clinicians -- knee arthroplasty -- hip fracture -- radiographs -- healthcare professionals -- distal radius fractures -- pathological fracture -- fragility fractures; Bones -- Surgery -- Periodicals; Joints -- Surgery -- Periodicals; Orthopedic surgery -- Periodicals; 617.47005
• DOI: 10.1302/0301-620X.103B12.BJJ-2021-0851.R1
• Languages: English
• ISSNs: 2049-4394

Abstract

Abstract : There is increasing popularity in the use of artificial intelligence and machine-learning techniques to provide diagnostic and prognostic models for various aspects of Trauma & Orthopaedic surgery. However, correct interpretation of these models is difficult for those without specific knowledge of computing or health data science methodology. Lack of current reporting standards leads to the potential for significant heterogeneity in the design and quality of published studies. We provide an overview of machine-learning techniques for the lay individual, including key terminology and best practice reporting guidelines. Cite this article: Bone Joint J 2021;103-B(12):1754–1758.